The influence that meteorological, climatological and environmental factors had on historical disease outbreaks is often speculated upon, but little investigated. Here, we explore potential associations between pandemic disease and climate over the last 2,500 years in Mediterranean history, focusing on ancient disease outbreaks and the Justinianic plague in particular. We underscore variation in the quality, quantity and interpretation of written evidence and proxy information from natural archives, the comlexity of identifying and disentangling past climatological and environmental drivers, and the need to integrate diverse methodologies to discern past climate-disease linkages and leverage historical experiences to prepare for the rapid expansion of novel pathogenic diseases. Although the difficulties entailed in establishing historical climate-pandemic linkages persist to the present, this is a research area as urgent as it is complex and historical perspectives are desperately needed.

The question of how sensitive the regional and local climates are to different land cover maps and fractions is important, as land cover affects the atmospheric circulation via its influence on heat, moisture, and momentum transfer, as well as the chemical composition of the atmosphere. In this study, we used three independent land cover data sets, GlobCover 2009, GLC2000 and ESACCI-LC, as the lower boundary of the regional climate model COSMO-CLM (Consortium for Small Scale Modeling in Climate Mode, v5.0-clm15) to perform convection-permitting regional climate simulations over the large part of Europe covering the years 1999 and 2000 at a 0.0275° horizontal resolution. We studied how the sensitivity of the impacts on regional and local climates is represented by different land cover maps and fractions, especially between warm (summer) and cold (winter) seasons. We show that the simulated regional climate is sensitive to different land cover maps and fractions. The simulated temperature and observational data are generally in good agreement, though with differences between the seasons. In comparison to winter, the summer simulations are more heterogeneous across the study region. The largest deviation is found for the alpine area (−3 to +3 °C), which might be among different reasons due to different classification systems in land cover maps and orographical aspects in the COSMO-CLM model. The leaf area index and plant cover also showed different responses based on various land cover types, especially over the area with high vegetation coverage. While relating the differences of land cover fractions and the COSMO-CLM simulation results (the leaf area index, and plant coverage) respectively, the differences in land cover fractions did not necessarily lead to corresponding bias in the simulation results. We finally provide a comparative analysis of how sensitive the simulation outputs (temperature, leaf area index, plant cover) are related to different land cover maps and fractions. The different regional representations of COSMO-CLM indicate that the soil moisture, atmospheric circulation, evaporative demand, elevation, and snow cover schemes need to be considered in the regional climate simulation with a high horizontal resolution.

Feedbacks of plant phenology to the regional climate system affect fluxes of energy, water, CO2, biogenic volatile organic compounds as well as canopy conductance, surface roughness length, and are influencing the seasonality of albedo. We performed simulations with the regional climate model COSMO-CLM (CCLM) at three locations in Germany covering the period 1999 to 2015 in order to study the sensitivity of grass phenology to different environmental conditions by implementing a new phenology module. We provide new evidence that the annually-recurring standard phenology of CCLM is improved by the new calculation of leaf area index (LAI) dependent upon surface temperature, day length, and water availability. Results with the new phenology implemented in the model show a significantly higher correlation with observations than simulations with the standard phenology. The interannual variability of LAI improves the representation of vegetation in years with extremely warm winter/spring (e.g., 2007) or extremely dry summer (e.g., 2003) and shows a more realistic growth period. The effect of the newly implemented phenology on atmospheric variables is small but tends to be positive. It should be used in future applications with an extension on more plant functional types.

Natural and human-historical archives document regional climate variations and extremes of the past 2500 years across the Eastern Mediterranean/Middle East. Earth System Models (ESM) can contribute to the interpretation of the variations in the paleoclimate data and the dynamics of the underlying mechanisms. State-of-the-art ESMs have a good temporal resolution but are spatially too coarse to adequately address regional processes. Here, we provide for the first time a regional climate model (RCM) output adjusted to past climates forcings covering the Eastern Mediterranean/Middle East at a 0.44° horizontal and up to hourly temporal resolution. CMIP6 external climate forcings of volcanic, orbital, solar and greenhouse-gas changes are implemented in the RCM COSMO-CLM (CCLM, COSMO 5.0 clm16). The sensitivity of the model to each of the implemented forcing is tested separately and in combination in a case study around the large Samalas volcanic eruption (1255–1264 CE) with strong socio-economic impacts in the study area. We evaluate the impact of the different implemented forcings compared with the standard CCLM model version for the present time. The orbital forcing is found to have the largest effect with cooler winter/spring and warmer autumn during the test period. The volcanic forcing has a strong cooling effect for a couple of years after the large volcanic eruption. Other climate forcings only show a smaller impact in the sensitivity study, while the improvements in simulated precipitation are mainly due to the higher spatial resolution than to a specific forcing. The study is the basis for the new 2500-year-long transient, fully forced RCM simulation. It offers an assessment of the implementation of forcings in the RCM, along with an enhancement of the simulations' skill through the use of the RCM. We aim to enhance our understanding of the role of single and joint forcings on climate variability and extremes, their underlying processes at the regional scale, potential climate-society interactions and address limitations and uncertainties.